30  Lake thermal structures II

Learning Objectives

After completing this tutorial you should be able to

Download the directory for this project here, make sure the directory is unzipped and move it to your bi328 directory. You can open the Rproj for this module either by double clicking on it which will launch Rstudio or by opening Rstudio and then using File > Open Project or by clicking on the Rproject icon in the top right of your program window and selecting Open Project.

There should be a file named 30_thermal-structure-II.qmd in that project directory. Use that file to work through this tutorial - you will hand in your rendered (“knitted”) quarto file as your homework assignment. So, first thing in the YAML header, change the author to your name. You will use this quarto document to record your answers. Remember to use comments to annotate your code; at minimum you should have one comment per code set1 you may of course add as many comments as you need to be able to recall what you did. Similarly, take notes in the document as we discuss discussion/reflection questions but make sure that you go back and clean them up for “public consumption”.

  • 1 You should do this whether you are adding code yourself or using code from our manual, even if it isn’t commented in the manual… especially when the code is already included for you, add comments to describe how the function works/what it does as we introduce it during the participatory coding session so you can refer back to it.

    • Before you get started, let’s make sure to read in all the packages we will need.

    # load libraries ----

# reporting
library(knitr)

# visualization
library(ggplot2)
library(ggthemes)
library(patchwork)

# data wrangling
library(dplyr)
library(tidyr)
library(readr)
library(skimr)
library(janitor)
library(magrittr)
library(lubridate)

# General Lake Model
library(GLMr)
library(glmtools)

# set other options ----
options(scipen=999)

knitr::opts_chunk$set(
  tidy = FALSE, 
  message = FALSE,
    warning = FALSE)

    30.1 Develop your own climate scenario

    Now that we have an idea of how to interact with the model, and we’ve tested the quality of our model by comparing observed and simulated data sets let’s use the model to simulate a data set for a specific climate model.

    You are going to develop two climate scenarios involving an extreme event (e.g. hurricane) and a more gradual change (e.g. increase in observed air temperature).

    For some inspiration on your climate scenario you can check out a visualization of high and low emission scenarios and how they impact temperature and precipitation. You can also take a look at global projections to see how much change is expected for various models and metrics.

    Consider this

    For each climate scenario describe which meteorological variables (air temperature, precipitation, wind) you will modify, by how much and why you are modifying them in this year, and when during your simulations you will modify the variables you have specified.

    Did it!

    [Your answer here]

    Next, you will need to modify the input data (met_hourly.csv) to reflect the climate scenario you want to run.

    Warning

    Make sure to follow the instructions on how to do this closely to make sure that you will be able to use your input file without issues to run the model.

    In your project folder create a new folder called sim1. Then make a copy of met_hourly.csv and place it into that folder and rename it sim1_met_hourly.csv. Be sure to do this before you open it and make any changes to the file. Also add a copy of glm2.nml to this folder.

    Warning

    Before you start - any time you open a *_met_hourly.csv file in Microsoft Excel it will alter the date/time formatting of the file so that GLM cannot read it. To avoid this error you will need to follow the steps below any time you change your met file.

    1. Just to be safe, copy and paste an extra version of the met_hourly.csv file in your sim folder so that you have a backup instead you make mistakes and want to go back to it. rename it met_hourly_original.csv or similar and be sure not to open this file in excel.
    2. Open the sim1/sim1_met_hourly.csv file in Excel. Manipulate the input variables to reflect the weather event/climate scenario you have designed2. The order of columns does not matter but you can only have one of for each variable and the columns names should not be changed.
    3. Once you have finished editing your file, reformat your date/time column.
      • highlight the time column in Excel
      • click on Format Cells and then on Custom
      • in the Type or Formatting box, change the default to YYYY-MM-DD hh:mm:ss exactly - this is the only format that GLM recognizes.
      • save the file once you are done and close the csv file - any time you open the file in Excel you will need to format this column and save the change!
    4. Read in the altered met_hourly file to Rstudio, convert the format of the date/time column and write it back out to file.

  • 2 A note on units: rain should be in meters per day, usually we measure precipitation in mm/d but make sure to convert to m/d by multiplying by 0.001 when you enter it into your met file

    • # make sure simulation 1 is correctly formatted ----

# read in altered met file & convert time format for GLM
metadata <- read_delim("sim1/sim1_met_hourly.csv", delim = ",") %>%
  mutate(time = as.POSIXct(strptime(time, "%Y-%m-%d %H:%M:%S", tz="EST")))
         
# write back to file
write_delim(metadata, "sim1/sim1_met_hourly.csv", delim = ",")

# make sure simlation 2 is correctly formatted ----

# read in altered met file & convert time format for GLM
metadata <- read_delim("sim2/sim2_met_hourly.csv", delim = ",") %>%
  mutate(time = as.POSIXct(strptime(time, "%Y-%m-%d %H:%M:%S", tz="EST")))
         
# write back to file
write_delim(metadata, "sim2/sim2_met_hourly.csv", delim = ",")
    Warning

    Any time you alter the met file you will need to go back through these steps!

    Before we can run the simulations, we will also need to edit the glm2.nml file to change the name of your input meteorological file so that it knows what file it should be looking for to get the meteorological data to run the model with.

    Open the nml file that you have placed in your sim1 folder in a text editor3 and scroll down to the meteorology section. Here, change the meteo_fl entry to your new file name (sim1_met_hourly.csv) for your climate scenario4.

  • 3 You can also open directly in Rstudios built in text editor if you prefer.

  • 4 Pro tip: Us the Find option to locate the nml file. Details count: Make sure that your quotes ' around the file name are upright and not slanted!

    • Once you’ve edited the met file name in your nml file, double check that you’ve made sure that it’s correct. You can do this by reading in the file.

    # change the path to new sim folder
nml_file <-  "sim1/glm2.nml"

# read altered nml file
nml <- read_nml(nml_file)

# check met file name
get_nml_value(nml, "meteo_fl")

    If you made the changes correctly the output should list the name of the meteorological file you specified that describes your weather/climate scenario (sim1_met_hourly.csv).

    Once you have completed this you are ready to run your climate scenarios.

    Repeat the process to create your second simulation.

    30.2 Run climate scenario 1

    Consider this

    Give a one sentence description of the scenario you are running and then outline how you would expect your climate change scenario to affect the thermal structure in Awesome Lake both in terms of water temperature and in stratification timing and strength.

    Did it!

    [Your answer here]

    Now, let’s run the scenario.

    # run model for climate scenario
run_glm("sim1/", verbose = TRUE)

    Our next step will be specifying the file path for our output so we can use the functions from glmtools to process the simulation output. Since this is our first climate scenario, we’ll name the variable climate1. Contrast this with our variable baseline that has the simulated output for current, typical meteorological conditions across a year based on the met_hourly.csv input file. Keep in mind that we use the same parameters to describe the link for both simulations - so we are keeping the lake the same and comparing different scenarios for the drivers of thermal structure5.

  • 5 The other type of comparison you could make is having the same conditions for drivers and two or more different lakes.

    • # define file path for output for baseline scenario
baseline <- file.path("sim", 'output.nc')

# define file path for output for climate scenario 1
climate1 <- file.path("sim1", "output.nc")

    Next, let’s pull the surface temperature for our first climate scenario6.

  • 6 Recall, that we did this earlier or our baseline scenario and stored that information in the data.frame surf_temp.

    • surf_temp_cl1 <- get_var(file = climate1, "temp", reference = "surface", z_out = c(1)) %>%
  rename(Scenario1_Temp = temp_1)

    30.3 Run climate scenario 2

    Consider this

    Give a one sentence description of the scenario you are running and then outline how you would expect your climate change scenario to affect the thermal structure in Awesome Lake both in terms of water temperature and in stratification timing and strength.

    Did it!

    [Your answer here]

    Now, let’s run the scenario. Make sure that your folder sim2 has the appropriate met data and that you have changed the filename in the nml file accordingly.

    # run model for climate scenario
run_glm("sim2/", verbose = TRUE)

    Again, we will want to specify the file path for our output - we’ll name this variable climate2. So now we have three file path variables baseline, climate1, and climate2. We will use those in the next section to plot the results of our simulations.

    # define file path for output
climate2 <- file.path("sim2", "output.nc")

    Next, let’s pull the surface temperature for our second scenario climate scenario.

    surf_temp_cl2 <- get_var(file = climate1, "temp", reference = "surface", z_out = c(1)) %>%
  rename(Scenario2_Temp = temp_1)

    30.4 Compare your results

    We have three scenarios to compare, your baseline which is the scenario based on current conditions, a simulation of a short-term climate/weather event (climate1), and a gradual change (climate2)7.

  • 7 Depending on how you set this up the names might be swapped for your scenarios

    • Give it a whirl

    Use your data wrangling skills to create a single data frame that contains the surface temperature for your baseline data and your two climate scenarios and create plot that allows you to compare how surface temperature changes over the simulated time period for all three scenarios.

    Did it!

    [Your answer here]

    We currently have our surface temperature data in three separate data sets. We want them to be in a single data set. The first step will be to combine all three data sets. We have a column in common (DateTime), so we will want to use left_join().

    Dates are notoriously messy to work with, so we will use mutate to make sure that all of them are in the same format first, using mutate().

    surf_temp <- surf_temp %>%
  mutate(DateTime = as.POSIXct(strptime(DateTime, "%Y-%m-%d", tz="EST")))

surf_temp_cl1 <- surf_temp_cl1 %>%
  mutate(DateTime = as.POSIXct(strptime(DateTime, "%Y-%m-%d", tz="EST")))

surf_temp_cl2 <- surf_temp_cl2 %>%
  mutate(DateTime = as.POSIXct(strptime(DateTime, "%Y-%m-%d", tz="EST")))

surf_temp_all <- surf_temp %>%
  left_join(surf_temp_cl1) %>%
  left_join(surf_temp_cl2)

    Before you can plot the data, you will need to create a tidy data set. Currently our column names for each of the scenarios are actually values, so it isn’t a tidy data set.

    tidy_temp <- surf_temp_all %>%
  pivot_longer(names_to = "scenarios", values_to = "temperature", 2:4)

    Now it will be easy to create your summary plot!

    Consider this

    Describe your results using the figure you just created. Remember to start with the general trends and patterns first and then highlight notable details. Be specific (consider things like min/max temperature, changes over time etc.).

    Did it!

    [Your answer here]

    Give it a whirl

    Create a plot showing the thermal structure of Lake Awesome for your baseline scenario, scenario 1 & 2.

    Did it!

    [Your answer here]

    You will be needing to use the function plot_temp() as we did above. Change the syntax accordingly. You will need to change the file argument to plot each scenario. Remember, we have stored the information for the output files of the simulations in the vectors baseline, climate1, and climate2.

    Remember, to use fig-height and fig-width to adjust the size of your figure so that you have an easily readable figure once you knit your document.

    Consider this

    Compare and contrast the thermal structure of Lake Awesome for the simulations of your baseline and two climate scenarios based on the figure above. Be specific - key things to think about are min/max temperatures, stratification vs isothermal, depth of the thermocline, how patterns change over time).

    Remember, this should be just a description - hang onto your discussion & interpretation of your results for the next question!

    Did it!

    [Your answer here]

    Consider this

    Discuss your results. Things to consider: Do your results support or contradict your hypotheses (how you were expecting the surface temperature and thermal structure to change for each scenario), what drivers did you manipulate and what mechanisms are in play in how they alter thermal structure (which cause heating/cooling/mixing?), consider temporal and spatial patterns (depth), what impacts might this have on organisms living in the lake?

    Did it!

    [Your answer here]

    Consider this

    Over the course of the semester we have discussed that (data) science is an iterative process, where we ask initial questions/formulate an initial hypothesis, test/explore it and then once we are done we generally have additional questions to explore.

    Based on what you have learned from running one simulation each for a gradual change (climate) and an extreme event (weather/climate), outline how would your refine your question and design and experiment running a series of simulations.

    Did it!

    [Your answer here]

    30.5 Acknowledgments

    These activities are based on the EDDIE Climate Change Effects on Lake Temperatures module.8

  • 8 Carey, C.C., S. Aditya, K. Subratie, V. Daneshmand, R. Figueiredo, and K.J. Farrell. 24 August 2020. Macrosystems EDDIE: Climate Change Effects on Lake Temperatures. Macrosystems EDDIE Module 1, Version 2.